modeling

New diagnostic tools leverage the power of Artificial Intelligence. Too Much Data to Process By 2030 all Baby Boomers will be over 65, which means that health-related issues become more and more top-of-mind for this aging population. Regular cancer screenings are one of these concerns. The sheer volume of such screenings already taxes existing healthcare systems. However, laboratories, diagnostic technicians, and healthcare providers are using powerful new technological tools to aid them in the work of helping patients live happier & healthier lives. One such tool is Artificial Intelligence, commonly referred to as AI. Unlike its counterparts depicted in the movies as sentient neural networks whose sole purpose is to destroy humanity, real AI has been a computing and data processing resource staple for decades. AI is as quotidian as the electric power grid and supermarkets. Everything from predictive weather modeling to aid meteorologists to CAD-based generative design for engineers, AI has proven to be a powerful tool for many industries in an everyday capacity. In the case of healthcare, data modeling and data processing have become synonymous with AI-driven environments capable of handling such massive volumes. Take for example liquid biopsies to better predict infant cancers. The data associated with these tests are referred to as high-throughput data. Making connections is essential between high-throughput data on orders of magnitude within a smaller outcome sample space of patient responses. The results of these AI-driven computations expedite determinations on whether or not they have cancer. Statistical models are useful for summarizing and describing variations to predictive models, and machine learning AI leverages these summaries that can make for more useful predictions, as seen above. Imaging for Data Collection and AI Processing From X-Rays, to CT (CAT) Scans, to MRIs, in vivo imaging technology has been one of the most powerful medical...